ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging2022
Principal component analysis denoising improves sensitivity of MR diffusion to detect white matter injury in neuroHIV.
Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Empirical noise mapping guides optimal region-of-interest selection for accurate noise estimation in prostate DWI.Journal of applied clinical medical physics · 2026Article
- The Individualized Prediction of Neurocognitive Function in People Living With HIV Based on Clinical and Multimodal Connectome Data.IEEE journal of biomedical and health informatics · 2023Article
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6 authors.
Funding
Abstract
background and purposeDiffusion-weighted imaging is able to capture important information about cerebral white matter (WM) structure. However, diffusion data can suffer from MRI and biological noise that degrades the quality of the images and makes finding important features difficult. We investigated how effectively local and nonlocal denoising increased the sensitivity to detect differences in cerebral WM in neuroHIV.
methodsWe utilized principal component analysis (PCA) denoising to detect WM differences using fractional anisotropy. Local and nonlocal PCA denoising paradigms were implemented that varied in search area and number of components. We examined different-sized WM tracts that consistently show differences between people living with Human Immunodeficiency Virus (HIV) (PWH) and HIV-negative individuals (corpus callosum, forceps minor, and right uncinate fasciculus), and size-matched tracts not typically associated with HIV-related differences (spinothalamic, right medial lemniscus, and left occipitopontine). We first conducted a full sample comparison of WM differences between groups, and then randomly reduced the sample to the point where we still found differences in WM.
resultsNonlocal PCA denoising allowed us to detect differences after a sample reduction of 35% in the forceps minor, 17% in the right uncinate fasciculus, and 6% in the corpus callosum.
conclusionsPCA denoising had a beneficial effect on detecting significant differences in PWH after sample size reduction. The smaller forceps minor tract and right uncinate fasciculus showed greater sensitivity to PCA denoising than the larger corpus callosum. These results show the importance of identifying the most effective PCA denoising strategy when investigating WM in PWH.
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